Prosumers’ optimal DER investments and DR usage for thermal and electrical loads in isolated microgrids

Martínez, Francisco Martín; Sánchez-Miralles, Álvaro; Rivier, Michel

doi:10.1016/j.epsr.2016.05.028

Cited by 17 publications

(12 citation statements)

References 9 publications

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“…Similar models to the one presented here can be found in [6], [44] and [160] , whose main variables and parameters are:…”

Section: Demand Response Modelmentioning

confidence: 97%

“…The total effective energy produced by the PV system can be calculated with (1) (taken from [152]), which is an approximation of PV power generation extensively used for DER system planning models, as in [6], [11] and [160].…”

Section: Photovoltaic (Pv) Modelmentioning

confidence: 99%

“…However, given the long-term scope of this study, it is considered that Energy Management of Smart Cities 39 Christian F. Calvillo Muñoz using an average value can deliver sensible results. Other works using a similar COP simplification can be found in [160], [164] and [165].…”

Section: Heat Pump (Hp) Modelmentioning

confidence: 99%

“…Other constraints are related to the SOC, setting a starting point (5) and a maximum capacity (8), and letting the battery system to transfer energy between days ((6) and (7)). This type of model is commonly used in the literature, as in [69], [160] or [166].…”

Section: Battery Modelmentioning

confidence: 99%

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5.12 Energy Management in Smart Cities

Fokaides

Apanavičienė

Klumbytė

2018

Comprehensive Energy Systems

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The Degree Certificates are giving reference to the joint programme. The doctoral candidates are jointly supervised, and must pass a joint examination procedure set up by the three institutions issuing the degrees. This Thesis is a part of the examination for the doctoral degree. The invested degrees are official in Spain, the Netherlands and Sweden, respectively. SETS Joint Doctorate was awarded the Erasmus Mundus excellence label by the European Commission in year 2010, and the European Commission's Education, Audiovisual and Culture Executive Agency, EACEA, has supported the funding of this programme. The EACEA is not to be held responsible for contents of the Thesis. vi Energy Management of Smart Cities Christian F. Calvillo Muñoz Energy Management of Smart Cities 1 Christian F. Calvillo Muñoz Chapter 7 Complete smart city energy model: aggregated DER and transport systems with active market participation Chapter 8 Conclusions, contributions and future work Chapter 1 Energy Management of Smart Cities 9 Christian F. Calvillo Muñoz 2 2 LITERATURE REVIEW This chapter presents the identification of energy systems task, corresponding to the smart city energy model methodology described in section 1.3. This includes the evaluation of energy systems available in a smart city, assessing their importance and possible synergies, in addition to the review of the existing models and tools, to select which ones could be used. The first part of the chapter introduces an overview of energy related work on planning and operation models within the smart city by classifying their scope into five main intervention areas. These areas are generation, storage, infrastructure, facilities, and transport. Then, more complex urban energy models integrating more than one intervention area are reviewed, outlining their advantages and limitations, existing trends and challenges, as well as some relevant smart cities applications. With this revision, important elements and possible synergies in the smart city are identified, and a framework for developing a comprehensive smart city energy model is proposed, along with some additional recommendations. Therefore, the main contributions of this chapter are, first, a comprehensive literature review of smart city energy systems and related modelling tools, classifying them in five intervention areas, and second, a framework with guidelines and recommendations for developing a comprehensive smart city energy model.

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“…Similar models to the one presented here can be found in [6], [44] and [160] , whose main variables and parameters are:…”

Section: Demand Response Modelmentioning

confidence: 97%

Section: Photovoltaic (Pv) Modelmentioning

confidence: 99%

Section: Heat Pump (Hp) Modelmentioning

confidence: 99%

Section: Battery Modelmentioning

confidence: 99%

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5.12 Energy Management in Smart Cities

Fokaides

Apanavičienė

Klumbytė

2018

Comprehensive Energy Systems

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“…From a technical point of view, the heating plants transformation, from combustion-based systems to the electrically-driven ones (i.e. HPs or hybrid systems), sometimes entails higher complexity and costs, making it unsuitable depending on the energy scenario boundary conditions; for that reason, the users availability to create the flexibility has to be evaluated [78] choosing also what are the most profitable investments to activate DR strategies [79,80]. Having said, a distinction between different buildings has to be made by the specific energy consumption and the installed technical systems:…”

Section: Storable Loads: Considerations On Buildings Characteristicsmentioning

confidence: 99%

Analysis on the potential of an energy aggregator for domestic users in the Italian electricity system

Santoli

Mancini

Basso

2019

Second International Conference on Material Science, Smart Structures and Applications: Icmss-2019

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This study is focused on the definition of an energy aggregator model for domestic users, offering the Demand Response (DR) optimization and additional services. The research project is based on the Italian electricity system analysis pointing out the renewables high contribution, the poor domestic users' electrification and the need to figure out those issues by a demand flexibility system implementation. Moreover, the characterization of Italian domestic users' energy loads allowed to identify the suitable strategies to get higher flexibility degree. Thus, three case studies have been considered to assess the achievable economic savings due to the DR implementation, accounting for the revenue for dispatching services as well as the intraday energy price variations on the Italian market. It emerged that the demand side flexibility implementation offers benefits to the Italian electricity system but entails small economic benefits for the endusers. Additionally, the adoption of such a system is quite complicated due to the lack of suitable and reliable automation devices. For that reason, a potential energy aggregator for domestic users will have to search funds for its own operation providing customers with additional services, such as energy saving and assisted-living, by using of ICT along with automation within each dwelling.

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